Identifying user relationships from situational analysis of user comments made on media content

The present disclosure generally relates to identifying relationships between users for filter-based searching of data available via information networks such as a wide area network (for example, the World Wide Web or the Internet), for example collaborative filtering.

The exponential growth of information available to users of various information networks (for example, broadcast, satellite, or cable television; wide area networks such as the World Wide Web or the Internet), requires organizing the presentation of the available information in an efficient and effective manner. Collaborative filtering attempts to organize presentation of information to a user in a wide area network (for example, the World Wide Web) based on automatically predicting the interests of a user by establishing relationships between items of interest to the user (for example, items recently viewed by the user at a commercial website) and other items that have been determined as of interest to other users. Item-based collaborative filtering, illustrated for example at the website “amazon.com” (users who bought x also bought y) is based on the premise that if a number of users purchase both items “x” and “y”, then another user viewing (or purchasing) the item “x” also may be interested in the item “y”.

Demographic based targeting classifies individuals according to demographics, in an attempt to provide content that is considered more appropriate to the target individuals. For example, technologies such as Choicestream (available at the website address “choicestream.com”) use a series of questions to categorize people into a set of clusters based on similar responses to the questions. Conventional collaborative filtering systems can establish a relationship between online users “A” and “B” based on both users entering the same rating value (five stars) for the same item “X”. Such collaborative filtering techniques have been used to determine cohorts (i.e., a group of individuals having similar tastes). An example of fixed cohorts (using fixed demographic data) is illustrated for example by the Claritas Prizm Clustering by Claritas, Inc., San Diego, Calif. Other examples of utilizing collaborative filtering to classify a user into a similar group are provided by companies such as Revenue Science (at the website address “www.revenuescience.com”), Aggregate Knowledge (at the website address “aggregateknowledge.com”), and Wunderloop (at the website address “wunderloop.com”).